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fix(video): keep text encoder dedup out of the registry rows

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The shared repo was written back onto the registry row, a module-level
singleton, so turning the setting off left the row pointing at the shared copy
for the rest of the session. It is chosen into locals instead.
This commit is contained in:
CalamitousFelicitousness
2026-08-12 02:03:06 +01:00
parent f01e752b06
commit 0d1882eca4
1 changed files with 21 additions and 31 deletions
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modules/video_models/video_load.py
+21 -31
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@@ -98,39 +98,29 @@ def load_model(selected: models_def.Model):
try:
load_args, quant_args = model_quant.get_dit_args({}, module='TE', device_map=True)
# loader deduplication of text-encoder models
if selected.te_cls.__name__ == 'T5EncoderModel' and shared.opts.te_shared_te:
selected.te = 'Disty0/t5-xxl'
selected.te_folder = ''
selected.te_revision = None
if selected.te_cls.__name__ == 'UMT5EncoderModel' and shared.opts.te_shared_te:
if 'SDNQ' in selected.name:
selected.te = 'Disty0/Wan2.2-T2V-A14B-SDNQ-uint4-svd-r32'
else:
selected.te = 'Wan-AI/Wan2.2-TI2V-5B-Diffusers'
selected.te_folder = 'text_encoder'
selected.te_revision = None
if selected.te_cls.__name__ == 'LlamaModel' and shared.opts.te_shared_te:
selected.te = 'hunyuanvideo-community/HunyuanVideo'
selected.te_folder = 'text_encoder'
selected.te_revision = None
if selected.te_cls.__name__ == 'Qwen2_5_VLForConditionalGeneration' and shared.opts.te_shared_te:
selected.te = 'ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers'
selected.te_folder = 'text_encoder'
selected.te_revision = None
if selected.te_cls.__name__ == 'Gemma3ForConditionalGeneration' and shared.opts.te_shared_te:
if 'SDNQ' in selected.name:
selected.te = 'OzzyGT/LTX-2.3-sdnq-dynamic-int4'
else:
selected.te = 'OzzyGT/LTX-2.3'
selected.te_folder = 'text_encoder'
selected.te_revision = None
# loader deduplication of text-encoder models: picked per load, not written back onto
# the registry row where it would outlive the setting
te_repo, te_folder, te_revision = selected.te, selected.te_folder, selected.te_revision
if shared.opts.te_shared_te:
te_cls_name = selected.te_cls.__name__
if te_cls_name == 'T5EncoderModel':
te_repo, te_folder, te_revision = 'Disty0/t5-xxl', '', None
elif te_cls_name == 'UMT5EncoderModel':
te_repo = 'Disty0/Wan2.2-T2V-A14B-SDNQ-uint4-svd-r32' if 'SDNQ' in selected.name else 'Wan-AI/Wan2.2-TI2V-5B-Diffusers'
te_folder, te_revision = 'text_encoder', None
elif te_cls_name == 'LlamaModel':
te_repo, te_folder, te_revision = 'hunyuanvideo-community/HunyuanVideo', 'text_encoder', None
elif te_cls_name == 'Qwen2_5_VLForConditionalGeneration':
te_repo, te_folder, te_revision = 'ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers', 'text_encoder', None
elif te_cls_name == 'Gemma3ForConditionalGeneration':
te_repo = 'OzzyGT/LTX-2.3-sdnq-dynamic-int4' if 'SDNQ' in selected.name else 'OzzyGT/LTX-2.3'
te_folder, te_revision = 'text_encoder', None
log.debug(f'Load video: module=te repo="{selected.te or selected.repo}" folder="{selected.te_folder}" cls={selected.te_cls.__name__} quant={model_quant.get_quant_type(quant_args)} loader={_loader("transformers")}')
log.debug(f'Load video: module=te repo="{te_repo or selected.repo}" folder="{te_folder}" cls={selected.te_cls.__name__} quant={model_quant.get_quant_type(quant_args)} loader={_loader("transformers")}')
kwargs["text_encoder"] = selected.te_cls.from_pretrained(
pretrained_model_name_or_path=selected.te or selected.repo,
subfolder=selected.te_folder,
revision=selected.te_revision or selected.repo_revision,
pretrained_model_name_or_path=te_repo or selected.repo,
subfolder=te_folder,
revision=te_revision or selected.repo_revision,
cache_dir=shared.opts.hfcache_dir,
**load_args,
**quant_args,